Title: Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks

Abstract

This paper proposes a distributed collaborative learning approach for cognitive and autonomous multi-domain elastic optical networking (EON). The proposed approach exploits a knowledge-defined networking framework which leverages a broker plane to coordinate the operations of multiple EON domains and applies machine learning (ML) to support autonomous and cognitive inter-domain service provisioning. By employing multiple distributed ML blocks learning domain-level features and working with broker plane aggregation ML blocks (through the chain rule-based training), the proposed approach enables to develop cognitive networking applications that can fully exploit the multi-domain EON states while obviating the need for the raw and confidential intra-domain data. In particular, we investigate end-to-end quality-of transmission estimation application using the distributed learning approach and propose three estimator designs incorporating the concepts of multi-task learning (MTL) and transfer learning (TL). Evaluations with experimental data demonstrate that the proposed designs can achieve estimation accuracies very close to (with differences less than 0.5%) or even higher than (with MTL/TL) those of the baseline models assuming full domain visibility.

Authors:

Chen, Xiaoliang ;

• Search DOE PAGES for author "Chen, Xiaoliang"
• Search DOE PAGES for ORCID "0000-0002-7805-6237"

Li, Baojia; Proietti, Roberto ;

• Search DOE PAGES for author "Proietti, Roberto"
• Search DOE PAGES for ORCID "0000-0001-6378-7005"

Liu, Che-Yu; Zhu, Zuqing ;

• Search DOE PAGES for author "Zhu, Zuqing"
• Search DOE PAGES for ORCID "0000-0002-4251-788X"

Ben Yoo, S. J.

Publication Date:

Wed Nov 20 00:00:00 EST 2019

Research Org.:

Univ. of California, Davis, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1607569

Alternate Identifier(s):

OSTI ID: 1803081

Grant/Contract Number:  

SC0016700

Resource Type:

Published Article

Journal Name:

Optics Express

Additional Journal Information:

Journal Name: Optics Express Journal Volume: 27 Journal Issue: 24; Journal ID: ISSN 1094-4087

Publisher:

Optical Society of America

Country of Publication:

United States

Language:

English

Subject:

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Optics